5 papers
Composition, Not Conversation: VLMs Lose the Scene, Not the Thread
L. D. M. S. Sai Teja, Ufaq Khan, N. Siva Gopala Krishna +5
Vision-language models (VLMs) increasingly reason over visual evidence that is cropped, segmented, retrieved, or revealed over time. Yet most VQA benchmarks present the complete im…
MVC-Bench: Benchmarking Calibration of Medical Vision-Language Models
Ashshak Sharifdeen, Shihab Aaqil Ahamed, Ufaq Khan +6
Reliable evaluation of vision-language models (VLMs) and medical vision-language models (Medical-VLMs) requires calibrated confidence, particularly under realistic clinical conditi…
On the Robustness of Temporal Vision-Language Models for Surgical Endoscopy Videos
Darakshan Rashid, Raza Imam, Ufaq Khan +9
Temporal vision-language models (TVLMs) offer a reusable, prompt-based interface for surgical video understanding, yet, their robustness under clinically realistic acquisition arti…
MedObvious: Exposing the Medical Moravec's Paradox in VLMs via Clinical Triage
Ufaq Khan, Umair Nawaz, L D M S S Teja +5
Vision Language Models (VLMs) are increasingly used for tasks like medical report generation and visual question answering. However, fluent diagnostic text does not guarantee safe…
AURORA: Adaptive Unified Representation for Robust Ultrasound Analysis
Ufaq Khan, L. D. M. S. Sai Teja, Ayuba Shakiru +4
Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinica…